SOCIO ECONIMIC CHARECTERISTICS OF LABOR FORCE OF RMG
CHAPTER 6 RESULTS AND FINDINGS
CHAPTER 6
Additionally, according to the first row of the frequency table of exploitation rate shows no exploitation in likely 20% readymade garments industries. Because it is interpreting like marginal productivity of labor is from 0.86 to 1.00 times higher than wage rate in one month. Indeed, which is indicating that marginal productivity of labor is no longer greater than the labor share or both are equal. Since this data collection bound in only one month, in any month some factories can face losses or on that month no exploitation can be take place. Besides some factories would also never put any kind of exploitation to the worker.
That’s why 20% of industries are not showing any kind of exploitation rate to the labor.
TABLE 17: Descriptive statistics of exploitation rate
N
Minimu m
Maximu
m Mean
Std.
Deviation Exploitation
rate 50 .8617 2.6434 1.32867
60 .3942954
The table 17 is expressing that the average exploitation rate is 1.3286 and the interpretation of that exploitation rate is that total marginal productivity of labor of a readymade garment factories are 1.3286 times higher than the total labor share of that factory. Where the minimum exploitation rate is 0.86 and highest exploitation rate is 2.64.
Perfectly competitive labor markets without distortionary taxes combined with a profit maximizing behavior of firms should imply that real wages should equal marginal product of labor (MPL). In this paper, we study the readymade garments industries and find that there exists a significant widening gap between real wages and marginal product of labor which is actually mentioning in this study as labor exploitation and that is nearly up to 2.64.
6.2 Determinants of labor exploitation:
Here we are analyzing econometric model which we used for describing the determinants of labor exploitation or explaining what factors are responsible for the labor exploitation of readymade garments sector and examine the effect of each determinant with significance level and the t statistics. The empirical result is both consistent and inconsistent with the theoretical postulation of the model. This clearly shows that the model is very strong reliable and high predictable ability. Again the no of observation is 50 and R Square is about 0.649 that is 64% of the variation in labor exploitation might explain by the model.
The following table is showing six variables as the determinants of the labor exploitations, they are age of the respondents, sex of the respondents, education level of the respondents, experience of the labor or total working year in ready-made garments industry, the membership of trade union in garments industry and the acknowledge of labor law. The coefficients, significance level and the t- ratio is given in the table too.
Table 18: Determinants of labor exploitation
Variables Coefficients Standard error T-ratio
Constant 1.94 0.53 3.65
Age -0.23 0.19 1.24
Sex -0.03 0.01 2.05
Education -0.19 0.06 2.98
Experience -0.11 0.05 2.23
Trade union -0.44 0.58 0.76
Labor law -0.29 0.26 1.13
Source: Authors’ own calculation
Y= 1.9 + 0.23 A -.03 S- 0.19 ED – 0.11 EX +.44 U +.29 L + ε
The coefficients of every variable which are determinants of labor exploitation; age, sex, education, experience, trade union, labor law are respectively -0.23, -0.03, -0.19, -0.11, -
0.44, -0.29. Here we can notice that all the coefficients are expressing negative coefficients.
In my study negative coefficients are better for my result, because it indicates that the change of independent variable causes changes in exploitation rate. And the standard error and the t statistics of those variable are respectively 0.19, 0.01, 0.06, 0.05, 0.58, 0.26 and 3.65, 1.24, 2.05, 2.98,2.23, 0.76, 1.13. It is clearly expressing that sex, education and experience both variables are statistically significant. And others variables are insignificant.
The regression model has positive intercept or constant mentioned as 1.94 which means that if all the independent variables have no effect on exploitation rate of labor but there has some amount exist might be treated as autonomous. Numerically, the respondents have 1.94-unit exploitation when all other factors have zero effect on exploitation rate or dependent variable in the mathematical sense.
The first independent variable is age of the respondent. In this regression analysis on exploitation rate shows the coefficient of age is - 0.23 which implies that if other variables are remaining unchanged the age has positive impact on labor exploitation. But the variable is significant at 10% level.
The second variable is sex of the respondent. Here the regression analysis on the exploitation rate shows negative coefficient is -0.03 which is negative; implies that if other variables are remaining unchanged the variable sex has direct impact on labor exploitation that is female are more exploited than the male. It is statistically significant at 5% level.
The third variable is education in my study and its coefficient is -0.19 which is negative. So that it interprets as the education level changed the exploitation rate is also getting changed. If education level of labor increases, then the exploitation rate will be decreased at 0.19 rate. This variable is significance at the level of 1%. So the education has a great impact on exploitation.
The fourth variable is experience of the respondent. Here the regression analysis on the exploitation rate shows negative coefficient is -0.11 which is negative; implies that if other variables are remaining unchanged the variable experience has direct impact on labor
exploitation that is the change experience in the ready-made garments industry or more skill will cause change in labor exploitation rate. If experience level of labor increases, then the exploitation rate will be decreased at 0.11 rate It is statistically significant at 5% level.
So the experience has a great impact on exploitation.
The fifth independent variable is the membership of trade union of the respondent. In this regression analysis on exploitation rate shows the coefficient is - 0.44 which implies that if other variables are remaining unchanged this variable has positive impact. But the result is showing that the data is statistically insignificant. As a result, participation at trade union does not have as much impact on labor exploitation as like education or experience or sex.
The sixth variable is the acknowledgement of labor law. The table is showing that this variable is not significant like trade union. So it does not have a greater impact on dependent variable of labor exploitation.
6.3. Socio economic condition and life standard of RMG labor:
The following table shows that the monthly average salary of a labor is TK. 9227.80 where highest TK. 15000 And lowest TK. 5000. It is observed that difference between highest and lowest labor salary is about 10,000. It depends on working experience, gender, activeness of trade union et cetera. With this very low level of wage, the average savings of labor is about TK 1642, where the highest total savings of a labor is TK 40,000 and the lowest total savings is TK. 0. It is showing that every labors total current savings is very low level. And average loan of a labor is tk. 1.37, where the highest and lowest amount of loan is respectively TK. 2,00,000 and 0. Here, in the table different average expenditure of a family of labor like health cost, education cost, cost regarding with cloth and food cost are respectively about TK. 1090.50, 757, 838.70, 6985 in a month. Sanitary system in this study is used as a dummy variable. The table indicating that the average sanitary system is 2.09 which is actually mean as bad sanitary system and of course every latrine is used by 8.04 individual averagely where the highest use of one latrine is by 20 people and lowest is by 1 person. Additionally, a room sharing and a kitchen sharing averagely calculated to respectively 2.89 person and 2.44 family.
Table 19: descriptive statistics of socio economic variables of RMG labors variables
N
Minimu m
Maximu
m Mean
Std.
Deviation
family member 100 1 9 3.74 1.587
amount of salary 100 5000 15000 9227.80 1935.704 amount of savings 100 0 40000 1642.00 4859.879
amount of loan 100 0 200000 1.37 29145.568
amount of house rent 100 1500 3200 2398.90 413.436 cost purpose of health
in month 100 100 5000 1090.50 885.167
cost purpose of
education in month 100 0 3000 757.00 949.881 cost purp0se of cloth
in month
100 100
2000
838.70 462.559
cost purpose of food in
month 100 2500 15000 6985.00 2746.398
sanitary system 100 1 3 2.09 .805
sharing a toilet 100 1 20 8.04 4.180
sharing a room 100 1 7 2.89 1.317
sharing a kitchen 100 1 5 2.44 .880
Source: Authors’ own calculation